A Comprehensive Review on Malware Detection Approaches
Omer Aslan,Refik Samet +1 more
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TLDR
This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches, and the pros and cons of each detection approach, and methods that are used in these approaches.Abstract:
According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques. In order to protect computer systems and the Internet from the malware, the malware needs to be detected before it affects a large number of systems. Recently, there have been made several studies on malware detection approaches. However, the detection of malware still remains problematic. Signature-based and heuristic-based detection approaches are fast and efficient to detect known malware, but especially signature-based detection approach has failed to detect unknown malware. On the other hand, behavior-based, model checking-based, and cloud-based approaches perform well for unknown and complicated malware; and deep learning-based, mobile devices-based, and IoT-based approaches also emerge to detect some portion of known and unknown malware. However, no approach can detect all malware in the wild. This shows that to build an effective method to detect malware is a very challenging task, and there is a huge gap for new studies and methods. This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches. Paper goal is to help researchers to have a general idea of the malware detection approaches, pros and cons of each detection approach, and methods that are used in these approaches.read more
Citations
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Journal ArticleDOI
A Survey on Malware Detection with Graph Representation Learning
TL;DR: In this article , the authors provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures, and demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers.
Proceedings ArticleDOI
Using Dtrace for Machine Learning Solutions in Malware Detection
TL;DR: This work uses Dtrace, a dynamic tracing framework recently introduced in Windows, to collect system call information from an affected system and builds a decision tree classifier that can detect malware using the sequences of system-calls made by malicious processes.
Journal ArticleDOI
MADS Based on DL Techniques on the Internet of Things (IoT): Survey
Hussah Talal,Rachid Zagrouba +1 more
TL;DR: In this paper, the authors presented a comprehensive study on security solutions in IoT applications, Intrusion Detection Systems (IDS), Malware Detection System (MDS), and the role of artificial intelligent (AI) in improving security in IoT.
Journal ArticleDOI
Self-Attentive Models for Real-Time Malware Classification
TL;DR: In this paper , two self-attention transformer-based classifiers, SeqConvAttn and ImgConvattn, are introduced to improve the performance of real-time malware classification.
Proceedings ArticleDOI
Using Side Channel Information and Artificial Intelligence for Malware Detection
TL;DR: In this article, side channel information leaked from hardware has been shown to reveal secret information in systems such as encryption keys, which can be used to detect malware running on a computing platform without access to the code involved.
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